Visual Data Story: Analysing Online Ads Engagement
Introduction
This project is based on data I collected while watching YouTube. I recorded details about the ads that appeared, including how long they were, what type they were, and whether they seemed personalised to me. The visualisations in this story help show some of the patterns in the ads I saw and explore how personalisation and ad types might relate to each other.
Data Summary
The summary of the data comes from the variables collected by logging ads that played while watching YouTube. For each ad I recorded how long it lasted, the type of ad it was, and whether it seemed personalised to my interests. This gave me a small but useful dataset of 40 variables that shows the variety and frequency of different ads, as well as some patterns around personalisation and timing.
Visualisation 1: Number of Ads by Type
This bar chart shows the number of times each kind of ad appeared while i was watching YouTube. It gives a quick overview of which ad types were shown the most. For example, it can help us see if skippable ads are more common than non skippable ones. This helps us understand what kind of ads users are more likely to come across.
Plot 1: Number of Ads by Type
This chart shows how many ads of each type I came across while watching YouTube. Each bar stands for a different ad category, and the taller the bar, the more often that type showed up. It helps give a quick idea of which ad types appeared the most.
Visualisation 2: Average ad length vs reletivity
This chart compares how long ads usually last based on whether they are personalized or not. It helps us understand if ads tailored to a viewer’s interests tend to be longer or shorter than those that aren’t personalised. From the results, you can see the average length of each ad type.
Visualisation 3: Number of ads seen per day
This graph displays how many ads were observed each day over the logging period. It highlights any increases or drops in ad viewing, which might reflect changes in user behaviour or online activity throughout the week.
Conclusion
The graphs in this project gave a clearer picture of the kinds of ads people come across on YouTube and how they might respond to them. From the first visual, we saw that some ad types show up more than others, which says a lot about what content is being pushed the most. The second graph pointed out that ads seen as not tailored to users were often the ones that lasted longer possibly because viewers found them more frustrating to sit through. The third graph tracked how many ads people reported seeing each day, showing how this can change over time.
Putting all this together, it seems like the format and timing of ads as well as how long they run—can affect how people react to them. Working through this task was a great chance to practise handling real data and turning it into visuals that tell a story, using what we’ve learned in STATS 220.
What’s going on with this data?
This dataset gives a glimpse into the kinds of ads that appear on YouTube and how often they show up. By exploring things like ad type, length, and timing, I wanted to find out whether personalised ads are more common, how long most ads last, and if certain types show up at specific times. The visualisations adove will hopefully help highlight some of these patterns and make it easier to understand what kinds of ads YouTube is showing and when.
Wait, another dancing cat?
A dance team of kittens!
